Evaluating the capability of Worldview-2 imagery for mapping alien tree species in a heterogeneous urban environment
Bibliographic Data
| ID | 22110402 |
|---|---|
| Authors | Simbarashe Jombo (0000-0002-5550-4877, University of the Witwatersrand, corresponding author), Elhadi Adam (0000-0003-3626-5839, University of the Witwatersrand), Marcus J Byrne (0000-0002-5155-2599, University of the Witwatersrand), Solomon W Newete (0000-0001-5245-8732, University of the Witwatersrand) |
| Editors | Danielle Sinnett (0000-0003-4757-3597, University of the West of England) |
| Year | 2020 |
| Volume | 6 |
| Issue | 1 |
| Publication date | 2020-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cogent Social Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2331-1886 • E-ISSN: 2331-1886 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/23311886.2020.1754146 |
| OpenAlex | W3023126754 |
| Language | EN |
| Citations received | 3 |
| References cited | 51 |
Street trees in urban planning have a long history as providers of an amicable environment for urban dwellers. Nevertheless, street trees are not always without a challenge, their ecosystem disservices include, inter alia, cracking pavements and foundations due to wandering tree roots that destroy concrete or asphalt surfaces. Thus, effective mapping of street trees assists in planning a suitable urban environment to improve city life. The traditional method for urban tree mapping is costly, time-consuming and labour intensive. However, commercially operated multi-spectral sensors, such as WorldView (WV) provide a more viable way to map trees at the species level. This study investigates the use of WV-2 imagery in the classification and mapping of five common alien street trees in a complex urban environment. It also examined the feasibility of Random Forest (RF) and Support Vector Machines (SVM) classifiers in mapping street trees in a heterogeneous urban environment. The classifiers produced an overall accuracy of 84.2 % for RF and 81.2 % for SVM. This study provides a detailed understanding of urban tree species to the municipality of Johannesburg and offers environmental managers an insight of classification methods for mapping trees using satellite imagery to comprehend their spatial distribution
Cartography · Civil engineering · Environmental planning · Environmental resource management · Geography · Remote sensing · Support vector machine · Urban ecosystem · Urban Environment · Urban forest · Urban park · Urban planning · Computer Science · Engineering · Environmental Science · Land Use and Ecosystem Services · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Artificial Intelligence · Forestry
Random forest classifier for remote sensing classification
A survey of image classification methods and techniques for improving classification performance
Random forest in remote sensing
Support vector machines in remote sensing
Green streets − Quantifying and mapping urban trees with street-level imagery and computer vision
Random Forests
Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research
Valuing green infrastructure in an urban environment under pressure — The Johannesburg case
Quantification of landscape transformation due to the Fast Track Land Reform Programme (FTLRP) in Zimbabwe using remotely sensed data
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,75 |
| Citation span | 2022 - 2024 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |